covid19 R Shiny notebook

# load dependencies
library(rgdal)

Loading required package: sp
rgdal: version: 1.4-8, (SVN revision 845)
 Geospatial Data Abstraction Library extensions to R successfully loaded
 Loaded GDAL runtime: GDAL 2.4.2, released 2019/06/28
 Path to GDAL shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/gdal
 GDAL binary built with GEOS: FALSE 
 Loaded PROJ.4 runtime: Rel. 5.2.0, September 15th, 2018, [PJ_VERSION: 520]
 Path to PROJ.4 shared files: /Library/Frameworks/R.framework/Versions/3.6/Resources/library/rgdal/proj
 Linking to sp version: 1.3-2 

library(geojsonio)

package ‘geojsonio’ was built under R version 3.6.2
Attaching package: ‘geojsonio’

The following object is masked from ‘package:base’:

    pretty

library(ggplot2)

Registered S3 method overwritten by 'dplyr':
  method           from
  print.rowwise_df     

library(leaflet)
library(plotly)

package ‘plotly’ was built under R version 3.6.2Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     

Attaching package: ‘plotly’

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    last_plot

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    filter

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    layout

library(RColorBrewer)
library(dplyr)

Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

library(broom)
library(mapproj)

Loading required package: maps

library(reshape2)

package ‘reshape2’ was built under R version 3.6.2

library(digest)
library(lubridate)

Attaching package: ‘lubridate’

The following object is masked from ‘package:base’:

    date

library(spdplyr)
library(rmapshaper)

package ‘rmapshaper’ was built under R version 3.6.2Registered S3 method overwritten by 'geojsonlint':
  method         from 
  print.location dplyr

library(raster)

package ‘raster’ was built under R version 3.6.2
Attaching package: ‘raster’

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    select

The following object is masked from ‘package:plotly’:

    select

library(mapview)

package ‘mapview’ was built under R version 3.6.2

library(sf)

package ‘sf’ was built under R version 3.6.2Linking to GEOS 3.7.2, GDAL 2.4.2, PROJ 5.2.0

# try this with: https://www.r-graph-gallery.com/168-load-a-shape-file-into-r.html

zipcode_boundaries <- readOGR("../ZIP_CODE_040114/ZIP_CODE_040114.shp",
  layer = "ZIP_CODE_040114", verbose = FALSE)

# try different table of zipcodes:
#alt_zipcode_boundaries <- readOGR("../alt_nyczip/nyc_zip_code_tabulation_areas_polygons.shp",
#  layer = "nyc_zip_code_tabulation_areas_polygons", verbose = FALSE)

# fortify the data to obtain dataframe for use by ggplot2
zipcode_boundaries_fortified <- tidy(zipcode_boundaries, region = "ZIPCODE")

# plot it
ggplot() +
  geom_polygon(data = zipcode_boundaries_fortified,
               aes(x = long, y = lat, group = group),
               fill = "#69b3a2", color="white") +
  theme_void()

# try using info from: https://nceas.github.io/oss-lessons/spatial-data-gis-law/3-mon-intro-gis-in-r.html

class(zipcode_boundaries)

extent(zipcode_boundaries)

plot(zipcode_boundaries, main = "NYC zipcode boundaries")

# convert spatial data frame to geojson
# nyc_json <- geojson_json(zipcode_boundaries)
# save to local file
# geojson_write(nyc_json, file = "./nyc.geojson")

alt_nyc_json <- geojson_json(alt_zipcode_boundaries)
geojson_write(alt_nyc_json, file = "./alt_nyc.geojson")

# create leaflet object
pal <- colorNumeric("viridis", NULL)

leaflet(alt_zipcode_boundaries) %>%
#  addProviderTiles(providers$OpenStreetMap) %>% 
  addTiles() %>% #add basemap
#  addPolygons(stroke = FALSE, smoothFactor = 0.3, fillOpacity = 1,
#    fillColor = ~pal(borough),
#    label = borough) %>%
  addPolylines(color = "#444444", 
               weight = 1, 
               smoothFactor = 0.5, 
               opacity = 1.0)

### run it all again but this time with SF (faster?)

zipcode_sf <- st_read("alt_nyczip/nyc_zip_code_tabulation_areas_polygons.shp")

Reading layer `nyc_zip_code_tabulation_areas_polygons' from data source `/Users/changhoyoon/Documents/Chang_Ho_Work/Master/Coursework/BMI706 data visualisation/viz_proj/COVID-19_NYC_706/alt_nyczip/nyc_zip_code_tabulation_areas_polygons.shp' using driver `ESRI Shapefile'
Simple feature collection with 262 features and 12 fields
geometry type:  POLYGON
dimension:      XY
bbox:           xmin: -74.25576 ymin: 40.49584 xmax: -73.6996 ymax: 40.91517
CRS:            4326

altzip_sf <- st_read("../ZIP_CODE_040114/ZIP_CODE_040114.shp")

Reading layer `ZIP_CODE_040114' from data source `/Users/changhoyoon/Documents/Chang_Ho_Work/Master/Coursework/BMI706 data visualisation/viz_proj/ZIP_CODE_040114/ZIP_CODE_040114.shp' using driver `ESRI Shapefile'
Simple feature collection with 263 features and 12 fields
geometry type:  POLYGON
dimension:      XY
bbox:           xmin: 913129 ymin: 120020.9 xmax: 1067494 ymax: 272710.9
CRS:            2263

ggplot() +
  geom_sf(data=zipcode_sf, aes(fill = borough)) 

# mapview function
# mapview(zipcode_sf)
mapview(altzip_sf)

# try leaflet with this
pal <- colorFactor(rainbow(5), zipcode_sf$borough)

leaflet(zipcode_sf) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%  #add simpler basemap 
  addPolygons(stroke = FALSE, 
              smoothFactor = 0.3, 
              fillOpacity = 0.2,
    highlight = highlightOptions(color = "white", weight = 10, bringToFront = TRUE))  %>%
  addPolylines(color = "#444444", 
               weight = 1, 
               smoothFactor = 0.5, 
               opacity = 1.0, 
               highlightOptions = highlightOptions(color = "white", 
                                                   weight = 5, 
                                                   bringToFront = F, 
                                                   opacity = 1))

### combine zipcode geometric data with cleaned dataframe

#cleaned_sf <- geojsonsf::geojson_sf("combined_covid_data.geojson")

#colnames(zipcode_sf)[which(names(zipcode_sf) == "postalcode")] <- "ZIPCODE"

# make certain of zipcode overlap between the two dataframes
setdiff(cleaned_sf$ZIPCODE, altzip_sf$ZIPCODE)

character(0)

# altzip only need ZIPCODE and geometry columns:
altzip_sf_compact <- subset(altzip_sf, select = c('ZIPCODE', 'geometry'))

combined_sf <- merge(cleaned_sf, altzip_sf_compact, by = 'ZIPCODE')

Error in merge.sf(cleaned_sf, altzip_sf_compact, by = "ZIPCODE") : 
  merge on two sf objects not supported

# 
cleaned_sf_compact <- within(cleaned_sf, rm('geometry'))
cleaned_sf_compact <- cleaned_sf_compact %>% st_set_geometry(NULL)
class(cleaned_sf_compact)

[1] "data.frame"

#combined_sf <- merge(cleaned_sf, altzip_sf_compact, by = 'ZIPCODE')

#combined_sf <- merge(altzip_sf_compact, cleaned_sf_compact, by = 'ZIPCODE')

class(combined_sf)

[1] "sf"         "data.frame"

mapview(combined_sf)

# let's try leaflet with this new combined_sf:

pal <- colorFactor(rainbow(5), combined_sf$PO_NAME)

leaflet(combined_sf) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%  #add simpler basemap 
  addPolygons(stroke = FALSE, 
              smoothFactor = 0.3, 
              fillOpacity = 0.2,
    highlight = highlightOptions(color = "white", weight = 10, bringToFront = TRUE))

sf layer is not long-lat datasf layer has inconsistent datum (+proj=lcc +lat_1=41.03333333333333 +lat_2=40.66666666666666 +lat_0=40.16666666666666 +lon_0=-74 +x_0=300000.0000000001 +y_0=0 +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=us-ft +no_defs ).
Need '+proj=longlat +datum=WGS84'

#  addPolylines(color = "#444444", 
#               weight = 1, 
#               smoothFactor = 0.5, 
#               opacity = 1.0, 
#               highlightOptions = highlightOptions(color = "white", 
#                                                   weight = 5, 
#                                                   bringToFront = F, 
#                                                   opacity = 1))